人工智能在肝脏疾病超声诊断中的应用
Application of Artificial Intelligence in Ultrasonic Diagnosis of Liver Diseases
DOI: 10.12677/ACM.2023.13102288, PDF,   
作者: 任 艳, 伍 杰, 王晓荣*:新疆医科大学第一附属医院腹部超声诊断科,新疆 乌鲁木齐
关键词: 人工智能深度学习肝脏疾病超声诊断Artificial Intelligence Deep Learning Hepatic Disease Ultrasonic Diagnosis
摘要: 超声检查无创、实时、价廉,无辐射、便于反复进行,是现在及其重要的医学影像学检查方法,是最常用的肝脏影像学检查方法。人工智能(AI)技术在超声中的运用,一方面提高了疾病诊断准确率,另一方面在一定程度上降低了人工成本。深度学习广泛应用于临床医学大数据分析领域,是一种高通量自动化提取高维度信息的新一代人工智能技术。深度学习可以对影像图像进行快速识别、精确分割,进行辅助诊断工作。本文就人工智能在肝脏疾病超声诊断中的运用展开论述。
Abstract: Ultrasound examination is noninvasive, real-time, inexpensive, no radiation, easy to repeat, now is an important medical imaging examination method, is the most commonly used liver imaging ex-amination method. The application of artificial intelligence (AI) technology in ultrasound, on the one hand, improves the accuracy of disease diagnosis, and on the other hand, reduces the labor costs to a certain extent. Deep learning is widely used in the field of big data analysis in clinical medicine, and it is a new generation of artificial intelligence technology with high-throughput au-tomated extraction of high-dimensional information. Deep learning can quickly identify and accu-rately segment images, and assist in diagnostic work. This paper discusses the application of artifi-cial intelligence in the ultrasound diagnosis of liver diseases
文章引用:任艳, 伍杰, 王晓荣. 人工智能在肝脏疾病超声诊断中的应用[J]. 临床医学进展, 2023, 13(10): 16355-16360. https://doi.org/10.12677/ACM.2023.13102288

参考文献

[1] Brody, H. (2013) Medical Imaging. Nature, 502, S81. [Google Scholar] [CrossRef
[2] 李媛, 张恩龙, 李文娟, 郎宁, 袁慧书. 人工智能在骨肌系统影像领域的研究进展[J]. 中国医学科学院学报, 2020, 42(2): 242-246.
[3] Haug, C.J. and Drazen, J.M. (2023) Artificial Intelligence and Machine Learning in Clinical Medicine, 2023. The New England Journal of Medicine, 388, 1201-1208. [Google Scholar] [CrossRef
[4] Liu, P.R., Lu, L., Zhang, J.Y., Huo, T.T., Liu, S.X. and Ye, Z.W. (2021) Application of Artificial Intelligence in Medicine: An Overview. Current Medical Science, 41, 1105-1115. [Google Scholar] [CrossRef] [PubMed]
[5] Yu, K.H., Beam, A.L. and Kohane, I.S. (2018) Artificial Intelligence in Health Care. Nature Biomedical Engineering, 2, 719-731. [Google Scholar] [CrossRef] [PubMed]
[6] 林广, 张志强. 人工智能医学影像在骨关节系统中的应用进展[J]. 中国医学影像学杂志, 2022, 30(2): 184-187.
[7] Wang, X., Yang, W., Weinreb, J., Han, J., Li, Q., Kong, X., Yan, Y., Ke, Z., Luo, B., Liu, T. and Wang, L. (2017) Searching for Prostate Cancer by Fully Automated Magnetic Resonance Imaging Classification: Deep Learning versus Non-Deep Learning. Scientific Reports, 7, Article No. 15415. [Google Scholar] [CrossRef] [PubMed]
[8] Ramesh, A.N., Kambhampati, C., Monson, J.R. and Drew, P.J. (2004) Artificial Intelligence in Medicine. Annals of The Royal College of Surgeons of England, 86, 334-338.
[9] Gao, S., Peng, Y., Guo, H., et al. (2014) Texture Analysis and Classification of Ultrasound Liver Imag-es. Bio-Medical Materials and Engineering, 24, 1209-1216. [Google Scholar] [CrossRef
[10] Hinton, G.E. and Salakhutdinov, R.R. (2006) Reducing the Dimensionality of Data with Neural Networks. Science, 313, 504-507. [Google Scholar] [CrossRef] [PubMed]
[11] Gore, J.C. (2020) Artificial Intelligence in Medical Imaging. Magnetic Resonance Imaging, 68, A1-A4. [Google Scholar] [CrossRef] [PubMed]
[12] Lee, H.W., Sung, J.J.Y. and Ahn, S.H. (2021) Artificial Intelligence in Liver Disease. Journal of Gastroenterology and Hepatology, 36, 539-542. [Google Scholar] [CrossRef] [PubMed]
[13] Zhou, L.Q., Wang, J.Y., Yu, S.Y., Wu, G.G., Wei, Q., Deng, Y.B., Wu, X.L., Cui, X.W. and Dietrich, C.F. (2019) Artificial Intelligence in Medical Imaging of the Liver. World Journal of Gas-troenterology, 25, 672-682. [Google Scholar] [CrossRef] [PubMed]
[14] 赵佳琦, 刁宗平, 徐琪, 章建全. 人工智能时代超声医学新发展[J]. 第二军医大学学报, 2019, 40(5): 478-482. [Google Scholar] [CrossRef
[15] Kahn Jr, C.E. (2017) From Images to Actions: Opportu-nities for Artificial Intelligence in Radiology. Radiology, 285, 719-720. [Google Scholar] [CrossRef] [PubMed]
[16] Hamet, P. and Tremblay, J. (2017) Artificial Intelligence in Medi-cine. Metabolism, 69, S36-S40. [Google Scholar] [CrossRef] [PubMed]
[17] Ambinder E.P. (2005) A History of the Shift toward Full Computerization of Medicine. Journal of Oncology Practice, 1, 54-56. [Google Scholar] [CrossRef] [PubMed]
[18] Lafaro, K.J., Demirjian, A.N. and Pawlik, T.M. (2015) Epidemiology of Hepatocellular Carcinoma. Surgical Oncology Clinics of North America, 24, 1-17. [Google Scholar] [CrossRef] [PubMed]
[19] Vivanti, R., Szeskin, A., Lev-Cohain, N., Sosna, J. and Joskowicz, L. (2017) Automatic Detection of New Tumors and Tumor Burden Evaluation in Longitudinal Liver CT Scan Studies. International Journal of Computer Assisted Radiology and Surgery, 12, 1945-1957. [Google Scholar] [CrossRef] [PubMed]
[20] Preis, O., Blake, M.A. and Scott, J.A. (2011) Neural Network Evaluation of PET Scans of the Liver: A Potentially Useful Adjunct in Clinical Interpretation. Radiology, 258, 714-721. [Google Scholar] [CrossRef] [PubMed]
[21] Liu, X., Song, J.L., Wang, S.H., Zhao, J.W. and Chen, Y.Q. (2017) Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification. Sensors, 17, Article 149. [Google Scholar] [CrossRef] [PubMed]
[22] Chou, R., Cuevas, C., Fu, R., et al. (2015) Imaging Techniques for the Diagnosis of Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Annals of Internal Medicine, 162, 697-711. [Google Scholar] [CrossRef
[23] Hu, H.T., Wang, W., Chen, L.D., Ruan, S.M., Chen, S.L., Li, X., Lu, M.D., Xie, X.Y. and Kuang, M. (2021) Artificial Intelligence Assists Identifying Malignant versus Benign Liver Le-sions Using Contrast-Enhanced Ultrasound. Journal of Gastroenterology and Hepatology, 36, 2875-2883. [Google Scholar] [CrossRef] [PubMed]
[24] Hassan, T.M., Elmogy, M. and Sallam, E.S. (2017) Diagnosis of Focal Liver Diseases Based on Deep Learning Technique for Ultrasound Images. Arabian Journal for Science and Engineer-ing, 42, 3127-3140. [Google Scholar] [CrossRef
[25] 蒲昆明, 李金花, 袁孟霞, 等. 四川省甘孜州北部地区肝包虫病超声诊断及分型[J]. 四川医学, 2020, 41(6): 640-643.
[26] WHO Informal Working Group (2003) International Classification of Ultrasound Images in Cystic Echinococcosis for Application in Clinical and Field Epidemiological Set-tings. Acta Tropica, 85, 253-261. [Google Scholar] [CrossRef
[27] Savelonas, M.A., Iakovidis, D.K., Legakis, I. and Maroulis, D. (20009) Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Im-ages. IEEE Transactions on Information Technology in Biomedicine, 13, 519-527. [Google Scholar] [CrossRef
[28] 曾涛, 戈杨, 李焕兴, 曾义岚. 肝包虫病超声图像特征及其诊断价值[J]. 热带医学杂志, 2019, 19(9): 1123-1126
[29] 南嘉格列, 李锐, 王海霞, 周旭, 王毅, 倪东. 基于深度学习的肝包虫病超声图像分型研究[J]. 深圳大学学报(理工版), 2019, 36(6): 702-708.
[30] LeCun, Y., Ben, Y. and Hinton, G. (2015) Deep Learning. Nature, 521, 436-444. [Google Scholar] [CrossRef] [PubMed]
[31] 王晓琳, 谢青. 非酒精性脂肪肝的继发因素及诊断[J]. 肝脏, 2022, 27(1): 109-113.
[32] Han, A., Byra, M., Heba, E., Andre, M.P., Erdman Jr, J.W., Loomba, R., Sirlin, C.B. and O’Brien Jr, W.D. (2020) Noninvasive Diagnosis of Nonalcoholic Fatty Liver Disease and Quantification of Liver Fat with Radio frequency Ultrasound Data Using One-dimensional Con-volutional Neural Networks. Radiology, 295, 342-350. [Google Scholar] [CrossRef] [PubMed]
[33] Biswas, M., Kuppili, V., Edla, D.R., Suri, H.S., Saba, L., Marin-hoe, R.T., Sanches, J.M. and Suri, J.S. (2018) Symtosis: A Liver Ultrasound Tissue Characterization and Risk Stratifica-tion in Optimized Deep Learning Paradigm. Computer Methods and Programs in Biomedicine, 155, 165-177. [Google Scholar] [CrossRef] [PubMed]
[34] Liao, Y.Y., Yeh, C.K., Huang, K.C., et al. (2019) Metabolic Characteristics of Novel Ultrasound Quantitative Diagnostic Index for Nonalcoholic Fatty Liver Disease. Scientific Re-ports, 9, Article No. 7922. [Google Scholar] [CrossRef] [PubMed]
[35] Wong, G.L., Yuen, P.C., Ma, A.J., Chan, A.W., Leung, H.H. and Wong, V.W. (2021) Artificial Intelligence in Prediction of Non-Alcoholic Fatty Liver Disease and Fibrosis. Journal of Gastroenterology and Hepatology, 36, 543-550. [Google Scholar] [CrossRef] [PubMed]